Consider a conceptual schema given below

Task 2 (23 marks) Consider a conceptual schema given below.

Your task is to perform logical database design, i.e. to transform a conceptual schema given above into a collection of relational schemes.

For each relational schema created clearly list the names of attributes, primary key, candidate keys (if any), and foreign keys (if any). Assume that the superset method must be used to implement a generalization (if any). A may how a conceptual schema can be transformed into a collection of relational schemes is explained in a presentation 06 Logical Design.

The relational schemes must be listed in a fonnat presented in the slides 44 and 45 in a presentation 06 Logical Design. Listing of the relational schemes in the other format scores no marks.

Deliverable:

A file solution . pdf with a list of relational schemes, primary key for each relational schema, candidate keys (if any) for each relational schema, foreign keys (if any) for each relational schema. Submission of a file with a different name and/or different extension and/or different type scores no marks.

D2I (Melbourne City)
The city of Melbourne has minute by minute directional pedestrian counts from pedestrian sensor devices located across the city. The data is updated every 15 minutes and can be used to determine variations in pedestrian activity throughout the day.

The aim of this project is to • visualise pedestrian traffic in CBD area where sensor is installed • find traffic trends for weekdays and weekends in Melbourne CBD • predict future traffic growth

You can find the link to the dataset for both sensor data and sensor locations: 1.://data.melbourne.vic.gov.au/browse? tags—sensors

Milestone Objectives:
Week 3: Perform a comprehensive survey about the problem and parameters involved for the types processing techniques you intend to use, including figuring out the different sub-problems/challenges that need to be tackled to reach a solution. Identify different datasets, the different machine leaming techniques used, and some other related image processing works that can be used to tackle this challenge.

Week 6: select some of the datasets and techniques from the literature for implementation. Implement the preprocessing and data wrangling task.
Week 9: Implement the existing basic methodologies and obtain experimental results
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Possible extension to SIT782:

Improved visualisation, and use of machine leaming/deep leaming to do prediction. Collaborate with other Squads if appropriate to integrate and feed in insights or statistics into other models, creating -meta-models”. Collaborate with other Masters students or organisations (with Capstone Leadership team approval) on potential remedies or other problems you have discovered

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